Artificial intelligence in healthcare: Technical advances, clinical integration, and future perspectives

Table 1 Summary of artificial intelligence applications in healthcare¹

Clinical domain	Representative AI architectures	Key clinical applications	Typical evidence level
Radiology	CNNs, GAN-based models	Nodule detection, image denoising, low-dose CT enhancement	Retrospective, in-silico validation
Cardiology	Deep CNNs, RNN-based models	Arrhythmia detection, echocardiographic segmentation, EF estimation	Large-scale retrospective studies
Oncology	Vision transformers, multimodal fusion models	Tumor grading, outcome prediction, treatment response monitoring	Retrospective cohorts, pilot clinical studies
Clinical Workflow	Large language models, multimodal foundation models	Report generation, study triage, clinical decision support	Emerging real-world deployments

¹Overview of artificial intelligence architectures and clinical applications across medical domains. While radiology and cardiology rely on established convolutional neural network frameworks, oncology and clinical workflows are increasingly integrating advanced multimodal and foundation models.

CNN: Convolutional neural network; AI: Artificial intelligence; GAN: Generative adversarial network; RNN: Recurrent neural network; EF: Ejection fraction; CT: Computed tomography.